Digital Engineering for Fusion: Uncertainty Quantification and Validation Frameworks
The University of York is offering a PhD position focused on advancing digital engineering for fusion power plants, with a particular emphasis on uncertainty quantification and validation frameworks. Fusion systems, especially those based on tokamak concepts, operate under extreme electromagnetic, thermal, and structural conditions. The project addresses the challenge of developing reliable predictive models for fusion magnet systems, which are central to plasma confinement and stability, and are subject to complex interactions between electrical currents, magnetic fields, and mechanical stresses.
As fusion technologies move from experimental devices to commercial power plants, simulation-driven design becomes increasingly important. However, uncertainties in material properties, boundary conditions, and operational parameters, combined with limited experimental data from full-scale fusion environments, restrict the credibility of these models. This project aims to develop robust methodologies that integrate experimental evidence with computational models to improve predictive accuracy and reliability.
The PhD research will focus on reducing uncertainty in multi-physics simulation models by integrating experimental measurements, statistical inference techniques, and digital twin architectures. Key objectives include developing inverse uncertainty quantification methods to refine model inputs using experimental data, establishing a hierarchical validation framework for multi-physics fusion systems, defining benchmark problems and datasets for simulation validation, and embedding these methodologies within a digital twin architecture. The ultimate goal is to demonstrate improved predictive capability for fusion magnet systems through data-driven model calibration.
The research approach is highly interdisciplinary, combining experimental, computational, and data-driven techniques. Experimental test rigs will generate validation datasets capturing electromagnetic and structural interactions, complemented by multi-physics simulations to model system behaviour under various operating conditions. The project will develop inverse uncertainty quantification techniques using approaches such as Bayesian inference and Gaussian process modelling, enabling systematic reduction of uncertainty and increased confidence in simulation outputs. A hierarchical validation framework will allow models to be tested across multiple levels of complexity, from simplified physical experiments to coupled multi-physics systems. These methodologies will be embedded within a digital twin framework, supporting predictive maintenance and operational decision-making.
The student will join a leading research environment at the University of York, benefiting from expertise in digital twins, data-centric engineering, multi-physics modelling and simulation, machine learning, uncertainty quantification, and complex engineering systems. The project is aligned with UK fusion initiatives and offers opportunities for collaboration with national laboratories, industry partners, and international research programmes.
Applicants should have a first-class or upper second-class (2.1) degree or equivalent in Engineering, Physics, Mathematics, or a related STEM discipline. A strong interest in fusion energy, digital twins, or data-driven engineering is required, along with knowledge of or willingness to develop skills in computational modelling, statistics/machine learning, and experimental methods. Experience in uncertainty quantification, multi-physics simulation, or data analysis is desirable but not essential.
To apply, use the University of York's online application system, selecting the PhD in Engineering for September 2026 entry and specifying your interest in this studentship. For informal enquiries, contact Prof S Perin at [email protected]. The application deadline is July 31, 2026.
